Jordi Madrenas

1.2k total citations
99 papers, 778 citations indexed

About

Jordi Madrenas is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Jordi Madrenas has authored 99 papers receiving a total of 778 indexed citations (citations by other indexed papers that have themselves been cited), including 87 papers in Electrical and Electronic Engineering, 34 papers in Biomedical Engineering and 25 papers in Artificial Intelligence. Recurrent topics in Jordi Madrenas's work include Advanced Memory and Neural Computing (31 papers), Advanced MEMS and NEMS Technologies (23 papers) and Analog and Mixed-Signal Circuit Design (22 papers). Jordi Madrenas is often cited by papers focused on Advanced Memory and Neural Computing (31 papers), Advanced MEMS and NEMS Technologies (23 papers) and Analog and Mixed-Signal Circuit Design (22 papers). Jordi Madrenas collaborates with scholars based in Spain, Japan and Italy. Jordi Madrenas's co-authors include Daniel Fernández, Eduard Alarcón, Michel Verleysen, P. P. Freitas, Susana Cardoso, C. Reig, Andrea De Marcellis, María‐Dolores Cubells‐Beltrán, Joana D. Santos and Joan Cabestany and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Jordi Madrenas

95 papers receiving 747 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jordi Madrenas Spain 16 553 243 155 137 117 99 778
Tadashi Shibata Japan 18 909 1.6× 204 0.8× 276 1.8× 181 1.3× 72 0.6× 141 1.3k
Alister Hamilton United Kingdom 14 484 0.9× 243 1.0× 200 1.3× 37 0.3× 160 1.4× 72 813
Yoshihito Amemiya Japan 15 740 1.3× 277 1.1× 112 0.7× 230 1.7× 71 0.6× 108 896
Paolo Fantini Italy 19 992 1.8× 74 0.3× 88 0.6× 117 0.9× 62 0.5× 81 1.2k
Takashi Morie Japan 16 1.2k 2.2× 400 1.6× 317 2.0× 70 0.5× 160 1.4× 177 1.5k
Philippe O. Pouliquen United States 13 515 0.9× 308 1.3× 163 1.1× 34 0.2× 114 1.0× 65 787
Farooq Ahmad Khanday India 20 1.5k 2.7× 356 1.5× 205 1.3× 181 1.3× 77 0.7× 123 1.9k
Amalia Miliou Greece 22 1.2k 2.1× 180 0.7× 208 1.3× 244 1.8× 140 1.2× 122 1.6k
Aydin Babakhani United States 22 1.7k 3.1× 369 1.5× 65 0.4× 162 1.2× 31 0.3× 132 1.9k
Seungchul Jung South Korea 12 671 1.2× 133 0.5× 107 0.7× 107 0.8× 39 0.3× 37 747

Countries citing papers authored by Jordi Madrenas

Since Specialization
Citations

This map shows the geographic impact of Jordi Madrenas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jordi Madrenas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jordi Madrenas more than expected).

Fields of papers citing papers by Jordi Madrenas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jordi Madrenas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jordi Madrenas. The network helps show where Jordi Madrenas may publish in the future.

Co-authorship network of co-authors of Jordi Madrenas

This figure shows the co-authorship network connecting the top 25 collaborators of Jordi Madrenas. A scholar is included among the top collaborators of Jordi Madrenas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jordi Madrenas. Jordi Madrenas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Moriya, Satoshi, Satoshi Ono, Hideaki Yamamoto, et al.. (2025). Analog VLSI Implementation of Subthreshold Spiking Neural Networks and Its Application to Reservoir Computing. IEEE Transactions on Circuits and Systems I Regular Papers. 72(10). 5571–5582. 1 indexed citations
2.
Madrenas, Jordi, et al.. (2024). Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware. Frontiers in Neuroscience. 18. 1425861–1425861. 1 indexed citations
3.
Moriya, Satoshi, et al.. (2024). Design of Mixed-Signal LSI with Analog Spiking Neural Network and Digital Inference Circuits for Reservoir Computing. UPCommons institutional repository (Universitat Politècnica de Catalunya). 1–6.
4.
Fernández, Daniel, et al.. (2023). CMOS Adaptive Optical Wireless Receiver for Ultra-Low-Power IoT Applications. 787–791. 1 indexed citations
5.
Fernández, Daniel, et al.. (2023). CMOS Wireless Hybrid Transceiver Powered by Integrated Photodiodes for Ultra-Low-Power IoT Applications. Electronics. 13(1). 28–28. 1 indexed citations
6.
Fernández, Daniel, et al.. (2022). Design, fabrication, characterization and reliability study of CMOS-MEMS Lorentz-force magnetometers. Microsystems & Nanoengineering. 8(1). 103–103. 9 indexed citations
7.
Moriya, Satoshi, et al.. (2021). Analog-circuit implementation of multiplicative spike-timing-dependent plasticity with linear decay. Nonlinear Theory and Its Applications IEICE. 12(4). 685–694. 3 indexed citations
8.
Fernández, Daniel, et al.. (2018). Experiments on MEMS Integration in 0.25 μm CMOS Process. Sensors. 18(7). 2111–2111. 15 indexed citations
9.
Madrenas, Jordi, et al.. (2018). Optimizing Power Consumption vs. Linearization in CMFB Amplifiers with Source Degeneration. 269–272. 6 indexed citations
10.
Fernández, Daniel, et al.. (2018). A Comprehensive High-Level Model for CMOS-MEMS Resonators. IEEE Sensors Journal. 18(7). 2632–2640. 7 indexed citations
11.
Sánchez, Giovanny, et al.. (2017). SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture. Neural Networks. 97. 28–45. 36 indexed citations
12.
Fernández, Daniel, et al.. (2017). Curvature of BEOL Cantilevers in CMOS-MEMS Processes. Journal of Microelectromechanical Systems. 26(4). 895–909. 11 indexed citations
13.
Fernández, Daniel, Elena Blokhina, Joan Pons-Nin, et al.. (2012). Pulsed Digital Oscillators for Electrostatic MEMS. IEEE Transactions on Circuits and Systems I Regular Papers. 59(12). 2835–2845. 9 indexed citations
14.
Moreno‐Eguilaz, Manuel & Jordi Madrenas. (2009). A reconfigurable architecture for emulating large-scale bio-inspired systems. 5216. 126–133. 3 indexed citations
15.
Fernández, Daniel, et al.. (2008). Pulse drive and capacitance measurement circuit for MEMS electrostatic actuators. Analog Integrated Circuits and Signal Processing. 57(3). 225–232. 9 indexed citations
16.
Madrenas, Jordi, et al.. (2004). BIOSEG: a bioinspired vlsi analog system for image segmentation.. The European Symposium on Artificial Neural Networks. 411–416. 3 indexed citations
17.
Madrenas, Jordi, et al.. (2004). Synchronization of Nonlinear Electronic Oscillators for Neural Computation. IEEE Transactions on Neural Networks. 15(5). 1315–1327. 30 indexed citations
18.
Madrenas, Jordi, et al.. (2003). Scene segmentation using neuromorphic oscillatory networks. IEEE Transactions on Neural Networks. 14(5). 1278–1296. 19 indexed citations
19.
Cabestany, Joan, Paolo Ienne, Juan‐Manuel Torres‐Moreno, & Jordi Madrenas. (1996). Is There a future for ANN Hardware. Infoscience (Ecole Polytechnique Fédérale de Lausanne).
20.
Madrenas, Jordi & Joan Cabestany. (1992). E-beam detector devices for IC controllability. Microelectronic Engineering. 16(1-4). 465–472. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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